COMPARISON OF THE EFFECTIVENESS OF THE GENETIC ALGORITHM AND THE MODIFIED LOUVAIN ALGORITHM FOR THE UNDERWATER WIRELESS SENSOR NETWORK CLUSTERING MODEL

Abstract

This paper presents a simulation model for managing the resources of an underwater wireless sensor network, based on the use of modularity as a metric in the clustering process. The model employs a modified Louvain algorithm, a genetic algorithm, and Dijkstra’s algorithm for constructing optimal message transmission routes. The main focus is on the comparative analysis of the efficiency of the developed algorithms. Simulation results confirm that the proposed model effectively addresses the challenges of energy-efficient clustering and reliable routing. Algorithms that aim to maximize modularity form stable and balanced cluster structures by accounting for the residual energy of sensors and are capable of supporting dynamic reclustering under changing topological or energy conditions. This leads to reduced data loss, balanced load distribution, and extended autonomous operation time of the network. The genetic algorithm, which uses modularity as a target function, demonstrates high adaptability to various network configurations and significantly reduces the number of message retransmissions during data collection. The comparative analysis shows that the genetic approach provides higher transmission reliability and energy efficiency compared to the Louvain algorithm, particularly under dynamically changing underwater conditions. The developed model can be applied to practical marine monitoring tasks where physical constraints, equipment characteristics, and the variability of network topology must be taken into account simultaneously.